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Deep-learning-based magnetic resonance (MR) imaging reconstruction techniques have the potential to accelerate MR image acquisition by reconstructing in real-time clinical quality images from k-spaces sampled at rates lower than specified by the Nyquist-Shannon sampling theorem, which is known as compressed sensing. In the past few years, several deep learning network architectures have been proposed for MR compressed sensing reconstruction. After examining the successful elements in thesedblp:conf/midl/SouzaLF19 fatcat:sgddvb3dkbdaxk457b42y3ebly